Large quantities of measurements and data collection are done in a fabrication plant (“fab”) based on equipment, process operations, and lots, to name a few examples. The data needs to be converted into meaningful reports consisting of statistical charts and indicators for analyzing process health and detecting process variations. Calculating summary statistics, process capability, and evaluation indicators on these measurements and data is a key qualification process that many organizations perform for every process generation in a fab. These calculations assist in measuring the performance of a process in the fab relative to product requirements, ensuring that the newly-developed process meets any necessary specifications, and certifying that the process is prepared for ramp up.
Currently, several home-grown applications and analysis scripts are utilized to produce the above-mentioned calculations. With these solutions, it can be difficult and time consuming to configure the settings that determine how the indicators are calculated. Furthermore, matching indicators and ensuring consistency between multiple fabs is not an easy job. Performance issues may be encountered due to the need to configure and manipulate a huge amount of data in different fashions. Similarly, it can be difficult to introduce new algorithms without impacting the existing architecture of the system. In addition, if a configuration and analysis library is utilized, it may not provide process engineers with all of the algorithms to analyze the data.
These prior art solutions are considered to be time consuming and unfeasible with the current size of wafers, number of process operations, and the amount of data collected in a typical fab. These solutions may result in calculation mismatches between different areas within a fab or between multiple fabs. Also, users are not able to control the batch jobs or run ad-hoc reports. Furthermore, the prior art solutions have significant data latency until results are produced. This is because they focus on web server and browser-based thin clients. On a daily basis, the data from cross-fabs would be rolled up. In some cases, this may involve 24 hours of data latency.
A solution that offers automatic configuration synchronization, end user control of when they want to schedule their batch jobs or generate ad-hoc reports, interactive charts and indicators, reduced response time, reduced configuration time, and reduced data latency, and an ability to solve generic problems for detecting process variations and equipment matching across multiple fabs would be beneficial.